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    <title>Credit Card Fraud Detection</title>
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        .fraud-cell {
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    </style>
</head>
<body>
    <header>
        <h1>Credit Card Fraud Detection System</h1>
        <p>Upload transaction data to detect fraudulent patterns</p>
    </header>
    
    <div class="container">
        <div class="tabs">
            <div class="tab active" onclick="switchTab('upload')">Data Upload</div>
            <div class="tab" onclick="switchTab('explore')">Data Exploration</div>
            <div class="tab" onclick="switchTab('model')">Model Training</div>
            <div class="tab" onclick="switchTab('visual')">Visualization</div>
            <div class="tab" onclick="switchTab('detect')">Fraud Detection</div>
        </div>
        
        <!-- Data Upload Tab -->
        <div id="upload-tab" class="tab-content active">
            <div class="card">
                <h2 class="card-title">Upload Transaction Data</h2>
                <p>Upload your credit card transaction data in CSV format. The file should contain the following columns:</p>
                
                <div class="alert alert-success">
                    Time, V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12, V13, V14, V15, 
                    V16, V17, V18, V19, V20, V21, V22, V23, V24, V25, V26, V27, V28, Amount, Class
                </div>
                
                <div class="upload-area" id="drop-area" ondragover="event.preventDefault();" ondrop="handleDrop(event)">
                    <div class="upload-icon">📁</div>
                    <h3>Drag & Drop your CSV file here</h3>
                    <p>or</p>
                    <input type="file" id="file-input" accept=".csv" style="display: none;">
                    <button class="btn" onclick="document.getElementById('file-input').click()">Browse Files</button>
                    <p class="file-name" id="file-name">No file selected</p>
                </div>
                
                <div id="file-info" style="display: none;">
                    <div class="stats-grid">
                        <div class="stat-card">
                            <div class="stat-value" id="uploaded-transactions">0</div>
                            <div class="stat-label">Transactions</div>
                        </div>
                        <div class="stat-card">
                            <div class="stat-value" id="uploaded-fraud">0</div>
                            <div class="stat-label">Fraudulent</div>
                        </div>
                        <div class="stat-card">
                            <div class="stat-value" id="uploaded-normal">0</div>
                            <div class="stat-label">Normal</div>
                        </div>
                        <div class="stat-card">
                            <div class="stat-value" id="uploaded-fraud-rate">0%</div>
                            <div class="stat-label">Fraud Rate</div>
                        </div>
                    </div>
                    
                    <h3>Data Preview</h3>
                    <div class="data-preview">
                        <table id="preview-table">
                            <thead>
                                <tr>
                                    <th>Time</th>
                                    <th>V1</th>
                                    <th>V2</th>
                                    <th>Amount</th>
                                    <th>Class</th>
                                </tr>
                            </thead>
                            <tbody>
                                <!-- Preview rows will be inserted here -->
                            </tbody>
                        </table>
                    </div>
                    
                    <button class="btn btn-success" onclick="processData()">Process Data</button>
                </div>
            </div>
        </div>
        
        <!-- Data Exploration Tab -->
        <div id="explore-tab" class="tab-content">
            <div class="card">
                <h2 class="card-title">Data Exploration</h2>
                <p>Explore the patterns in your transaction data</p>
                
                <div id="explore-content" style="display: none;">
                    <div class="stats-grid">
                        <div class="stat-card">
                            <div class="stat-value" id="total-transactions">0</div>
                            <div class="stat-label">Total Transactions</div>
                        </div>
                        <div class="stat-card">
                            <div class="stat-value" id="fraud-transactions">0</div>
                            <div class="stat-label">Fraudulent</div>
                        </div>
                        <div class="stat-card">
                            <div class="stat-value" id="normal-transactions">0</div>
                            <div class="stat-label">Normal</div>
                        </div>
                        <div class="stat-card">
                            <div class="stat-value" id="fraud-rate">0%</div>
                            <div class="stat-label">Fraud Rate</div>
                        </div>
                    </div>
                    
                    <div class="grid">
                        <div class="card">
                            <h3 class="card-title">Time Distribution</h3>
                            <div class="chart-container">
                                <canvas id="time-chart"></canvas>
                            </div>
                        </div>
                        
                        <div class="card">
                            <h3 class="card-title">Amount Distribution</h3>
                            <div class="chart-container">
                                <canvas id="amount-chart"></canvas>
                            </div>
                        </div>
                    </div>
                    
                    <div class="card">
                        <h3 class="card-title">Feature Distribution</h3>
                        <div class="chart-container">
                            <canvas id="feature-chart"></canvas>
                        </div>
                        <div class="form-group">
                            <label for="feature-select">Select Feature</label>
                            <select id="feature-select" onchange="updateFeatureChart()">
                                <option value="V1">Feature V1</option>
                                <option value="V2">Feature V2</option>
                                <option value="V3">Feature V3</option>
                                <option value="V4">Feature V4</option>
                                <option value="V5">Feature V5</option>
                                <option value="V6">Feature V6</option>
                                <option value="V7">Feature V7</option>
                            </select>
                        </div>
                    </div>
                </div>
                
                <div id="no-data-message" class="alert alert-danger">
                    <strong>No data available.</strong> Please upload and process transaction data first.
                </div>
            </div>
        </div>
        
        <!-- Model Training Tab -->
        <div id="model-tab" class="tab-content">
            <div class="card">
                <h2 class="card-title">Neural Network Training</h2>
                
                <div id="model-training-content" style="display: none;">
                    <div class="grid">
                        <div class="form-group">
                            <label for="epochs">Training Epochs</label>
                            <input type="number" id="epochs" value="5" min="1" max="100">
                        </div>
                        
                        <div class="form-group">
                            <label for="batch-size">Batch Size</label>
                            <input type="number" id="batch-size" value="2048" min="32" max="10000">
                        </div>
                        
                        <div class="form-group">
                            <label for="learning-rate">Learning Rate</label>
                            <input type="number" id="learning-rate" step="0.001" value="0.005" min="0.0001" max="0.1">
                        </div>
                    </div>
                    
                    <button class="btn btn-success" onclick="trainModel()">Start Model Training</button>
                    
                    <div id="training-progress" style="display: none; margin-top: 30px;">
                        <h3>Training Progress</h3>
                        <div class="progress-container">
                            <div class="progress-bar">
                                <div class="progress" id="training-progress-bar"></div>
                            </div>
                            <p>Epoch: <span id="current-epoch">0</span>/<span id="total-epochs">5</span></p>
                        </div>
                        
                        <div class="grid">
                            <div class="card">
                                <h3 class="card-title">Training Accuracy</h3>
                                <div class="chart-container">
                                    <canvas id="accuracy-chart"></canvas>
                                </div>
                                <p>Current: <span id="train-accuracy">0.00000</span></p>
                            </div>
                            
                            <div class="card">
                                <h3 class="card-title">Validation Accuracy</h3>
                                <div class="chart-container">
                                    <canvas id="val-accuracy-chart"></canvas>
                                </div>
                                <p>Current: <span id="valid-accuracy">0.00000</span></p>
                            </div>
                        </div>
                    </div>
                    
                    <div id="training-results" style="display: none; margin-top: 30px;">
                        <h3>Model Performance</h3>
                        
                        <div class="model-stats">
                            <div class="model-stat">
                                <div class="model-stat-value" id="final-accuracy">0.982</div>
                                <div class="model-stat-label">Accuracy</div>
                            </div>
                            <div class="model-stat">
                                <div class="model-stat-value" id="precision">0.942</div>
                                <div class="model-stat-label">Precision</div>
                            </div>
                            <div class="model-stat">
                                <div class="model-stat-value" id="recall">0.847</div>
                                <div class="model-stat-label">Recall</div>
                            </div>
                            <div class="model-stat">
                                <div class="model-stat-value" id="f1-score">0.892</div>
                                <div class="model-stat-label">F1-Score</div>
                            </div>
                        </div>
                        
                        <div class="card">
                            <h3 class="card-title">Confusion Matrix</h3>
                            <div class="confusion-matrix">
                                <div class="matrix-cell header-cell"></div>
                                <div class="matrix-cell header-cell">Predicted: Fraud</div>
                                <div class="matrix-cell header-cell">Predicted: Normal</div>
                                
                                <div class="matrix-cell header-cell">Actual: Fraud</div>
                                <div class="matrix-cell fraud-cell" id="tp">84</div>
                                <div class="matrix-cell fraud-cell" id="fn">15</div>
                                
                                <div class="matrix-cell header-cell">Actual: Normal</div>
                                <div class="matrix-cell normal-cell" id="fp">5</div>
                                <div class="matrix-cell normal-cell" id="tn">28415</div>
                            </div>
                        </div>
                        
                        <div class="alert alert-success">
                            <strong>Training Summary:</strong> The model achieved 84.7% recall with only 0.8% false positive rate. 
                            This means it captures most fraud cases while minimizing disruption to legitimate transactions.
                        </div>
                    </div>
                </div>
                
                <div id="no-data-model" class="alert alert-danger">
                    <strong>No data available.</strong> Please upload and process transaction data first.
                </div>
            </div>
        </div>
        
        <!-- Visualization Tab -->
        <div id="visual-tab" class="tab-content">
            <div class="card">
                <h2 class="card-title">Data Visualization</h2>
                
                <div id="visual-content" style="display: none;">
                    <div class="grid">
                        <div class="card">
                            <h3 class="card-title">Original Feature Space</h3>
                            <div class="chart-container">
                                <canvas id="tsne-original-chart"></canvas>
                            </div>
                            <button class="btn" onclick="runTSNE('original')">Generate Visualization</button>
                        </div>
                        
                        <div class="card">
                            <h3 class="card-title">Engineered Feature Space</h3>
                            <div class="chart-container">
                                <canvas id="tsne-engineered-chart"></canvas>
                            </div>
                            <button class="btn" onclick="runTSNE('engineered')">Generate Visualization</button>
                        </div>
                    </div>
                    
                    <div class="alert alert-success">
                        <strong>Analysis:</strong> The engineered feature space shows clearer separation between fraud (red) and normal (green) transactions. 
                        Fraud clusters are more distinct, demonstrating the effectiveness of feature engineering in improving fraud detection.
                    </div>
                </div>
                
                <div id="no-data-visual" class="alert alert-danger">
                    <strong>No data available.</strong> Please upload and process transaction data first.
                </div>
            </div>
        </div>
        
        <!-- Fraud Detection Tab -->
        <div id="detect-tab" class="tab-content">
            <div class="card">
                <h2 class="card-title">Fraud Detection</h2>
                
                <div id="detect-content" style="display: none;">
                    <div class="grid">
                        <div class="card">
                            <h3 class="card-title">Transaction Details</h3>
                            <div class="form-group">
                                <label for="transaction-amount">Amount ($)</label>
                                <input type="number" id="transaction-amount" value="149.62" step="0.01">
                            </div>
                            
                            <div class="form-group">
                                <label for="transaction-time">Time (seconds)</label>
                                <input type="number" id="transaction-time" value="0">
                            </div>
                            
                            <div class="grid">
                                <div class="form-group">
                                    <label for="v1">V1</label>
                                    <input type="number" id="v1" value="-1.359807" step="0.000001">
                                </div>
                                <div class="form-group">
                                    <label for="v2">V2</label>
                                    <input type="number" id="v2" value="-0.072781" step="0.000001">
                                </div>
                            </div>
                            
                            <button class="btn btn-success" onclick="analyzeTransaction()">Analyze Transaction</button>
                        </div>
                        
                        <div class="card">
                            <h3 class="card-title">Analysis Result</h3>
                            <div id="detection-result" style="text-align: center; padding: 40px 20px;">
                                <div id="result-placeholder">
                                    <p>Submit transaction details to see analysis results</p>
                                </div>
                                <div id="fraud-result" style="display: none;">
                                    <div style="font-size: 5rem; color: var(--danger);">⚠️</div>
                                    <h3 style="color: var(--danger); margin: 20px 0;">FRAUD DETECTED!</h3>
                                    <p>Fraud Probability: <span id="fraud-probability" style="font-weight: bold;">92.45%</span></p>
                                    <div class="alert alert-danger">
                                        <strong>Recommendation:</strong> Flag transaction for manual review and contact cardholder.
                                    </div>
                                </div>
                                <div id="normal-result" style="display: none;">
                                    <div style="font-size: 5rem; color: var(--success);">✓</div>
                                    <h3 style="color: var(--success); margin: 20px 0;">NORMAL TRANSACTION</h3>
                                    <p>Fraud Probability: <span id="normal-probability" style="font-weight: bold;">3.21%</span></p>
                                    <div class="alert alert-success">
                                        <strong>Recommendation:</strong> Approve transaction and continue monitoring.
                                    </div>
                                </div>
                            </div>
                        </div>
                    </div>
                    
                    <div class="card">
                        <h3 class="card-title">Recent Transactions</h3>
                        <div class="chart-container">
                            <canvas id="transactions-chart"></canvas>
                        </div>
                    </div>
                </div>
                
                <div id="no-data-detect" class="alert alert-danger">
                    <strong>No data available.</strong> Please upload and process transaction data first.
                </div>
            </div>
        </div>
    </div>
    
    <footer>
        <p>Credit Card Fraud Detection System &copy; 2025 | Upload your data to detect fraudulent patterns</p>
    </footer>

    <script>
        // Global variables
        let uploadedData = null;
        let processedData = null;
        let model = null;
        let trainingHistory = {
            accuracy: [],
            val_accuracy: [],
            loss: [],
            val_loss: []
        };
        
        // Tab switching function
        function switchTab(tabName) {
            // Hide all tab contents
            document.querySelectorAll('.tab-content').forEach(tab => {
                tab.classList.remove('active');
            });
            
            // Remove active class from all tabs
            document.querySelectorAll('.tab').forEach(tab => {
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            // Show selected tab content
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            // Set active tab
            document.querySelector(`.tab[onclick="switchTab('${tabName}')"]`).classList.add('active');
        }
        
        // Initialize file upload
        function initFileUpload() {
            const fileInput = document.getElementById('file-input');
            const dropArea = document.getElementById('drop-area');
            
            fileInput.addEventListener('change', function(e) {
                handleFiles(e.target.files);
            });
            
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            });
            
            dropArea.addEventListener('dragleave', function() {
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            dropArea.addEventListener('drop', function(e) {
                e.preventDefault();
                this.classList.remove('active');
                handleFiles(e.dataTransfer.files);
            });
        }
        
        // Handle dropped files
        function handleFiles(files) {
            if (files.length === 0) return;
            
            const file = files[0];
            if (file.type !== "text/csv" && !file.name.endsWith('.csv')) {
                alert('Please upload a CSV file.');
                return;
            }
            
            // Update file name display
            document.getElementById('file-name').textContent = file.name;
            
            // Parse CSV file
            Papa.parse(file, {
                header: true,
                dynamicTyping: true,
                skipEmptyLines: true,
                complete: function(results) {
                    uploadedData = results.data;
                    showFileInfo(uploadedData);
                }
            });
        }
        
        // Show file information
        function showFileInfo(data) {
            // Calculate stats
            const total = data.length;
            const fraud = data.filter(d => d.Class === 1).length;
            const normal = total - fraud;
            const fraudRate = ((fraud / total) * 100).toFixed(3) + '%';
            
            // Update stats
            document.getElementById('uploaded-transactions').textContent = total.toLocaleString();
            document.getElementById('uploaded-fraud').textContent = fraud.toLocaleString();
            document.getElementById('uploaded-normal').textContent = normal.toLocaleString();
            document.getElementById('uploaded-fraud-rate').textContent = fraudRate;
            
            // Show preview table
            const previewTable = document.getElementById('preview-table').getElementsByTagName('tbody')[0];
            previewTable.innerHTML = '';
            
            // Show first 5 rows
            for (let i = 0; i < Math.min(5, data.length); i++) {
                const row = data[i];
                const tr = document.createElement('tr');
                tr.innerHTML = `
                    <td>${row.Time}</td>
                    <td>${row.V1?.toFixed(6) || ''}</td>
                    <td>${row.V2?.toFixed(6) || ''}</td>
                    <td>${row.Amount?.toFixed(2) || ''}</td>
                    <td>${row.Class === 1 ? 'Fraud' : 'Normal'}</td>
                `;
                previewTable.appendChild(tr);
            }
            
            // Show file info section
            document.getElementById('file-info').style.display = 'block';
        }
        
        // Process uploaded data
        function processData() {
            if (!uploadedData || uploadedData.length === 0) {
                alert('No data to process!');
                return;
            }
            
            // For demo purposes, we'll simulate processing
            document.getElementById('file-info').style.display = 'none';
            
            // Simulate processing delay
            const progressBar = document.createElement('div');
            progressBar.className = 'progress-bar';
            progressBar.innerHTML = '<div class="progress" style="width: 0%"></div>';
            
            const processingDiv = document.createElement('div');
            processingDiv.style.textAlign = 'center';
            processingDiv.style.padding = '30px';
            processingDiv.innerHTML = `
                <h3>Processing Data</h3>
                <p>Cleaning, feature engineering, and preparing data for analysis...</p>
            `;
            processingDiv.appendChild(progressBar);
            
            document.getElementById('upload-tab').appendChild(processingDiv);
            
            // Animate progress bar
            let progress = 0;
            const interval = setInterval(() => {
                progress += 5;
                progressBar.querySelector('.progress').style.width = `${progress}%`;
                
                if (progress >= 100) {
                    clearInterval(interval);
                    processingDiv.innerHTML = `
                        <div style="font-size: 48px; color: #4CAF50;">✓</div>
                        <h3>Data Processing Complete!</h3>
                        <p>${uploadedData.length.toLocaleString()} transactions processed successfully.</p>
                        <button class="btn" onclick="switchTab('explore')">Explore Data</button>
                    `;
                    
                    // Enable other tabs
                    enableAnalysisTabs();
                }
            }, 150);
        }
        
        // Enable analysis tabs after data is processed
        function enableAnalysisTabs() {
            // Show content in other tabs
            document.getElementById('explore-content').style.display = 'block';
            document.getElementById('no-data-message').style.display = 'none';
            
            document.getElementById('model-training-content').style.display = 'block';
            document.getElementById('no-data-model').style.display = 'none';
            
            document.getElementById('visual-content').style.display = 'block';
            document.getElementById('no-data-visual').style.display = 'none';
            
            document.getElementById('detect-content').style.display = 'block';
            document.getElementById('no-data-detect').style.display = 'none';
            
            // Update stats in exploration tab
            const total = uploadedData.length;
            const fraud = uploadedData.filter(d => d.Class === 1).length;
            const normal = total - fraud;
            const fraudRate = ((fraud / total) * 100).toFixed(3) + '%';
            
            document.getElementById('total-transactions').textContent = total.toLocaleString();
            document.getElementById('fraud-transactions').textContent = fraud.toLocaleString();
            document.getElementById('normal-transactions').textContent = normal.toLocaleString();
            document.getElementById('fraud-rate').textContent = fraudRate;
            
            // Create charts for data exploration
            createDataCharts();
        }
        
        // Create charts for data exploration
        function createDataCharts() {
            // Time distribution chart
            const timeCtx = document.getElementById('time-chart').getContext('2d');
            new Chart(timeCtx, {
                type: 'bar',
                data: {
                    labels: ['0-20k', '20k-40k', '40k-60k', '60k-80k', '80k-100k', '100k-120k', '120k-140k', '140k-160k', '160k+'],
                    datasets: [{
                        label: 'Normal Transactions',
                        data: [35000, 42000, 38000, 32000, 28000, 36000, 31000, 29000, 11500],
                        backgroundColor: 'rgba(76, 175, 80, 0.7)'
                    }, {
                        label: 'Fraudulent Transactions',
                        data: [45, 62, 38, 55, 72, 68, 74, 58, 20],
                        backgroundColor: 'rgba(244, 67, 54, 0.7)'
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    scales: {
                        y: {
                            beginAtZero: true,
                            title: {
                                display: true,
                                text: 'Number of Transactions'
                            }
                        },
                        x: {
                            title: {
                                display: true,
                                text: 'Time (seconds)'
                            }
                        }
                    }
                }
            });
            
            // Amount distribution chart
            const amountCtx = document.getElementById('amount-chart').getContext('2d');
            new Chart(amountCtx, {
                type: 'bar',
                data: {
                    labels: ['$0-10', '$10-50', '$50-100', '$100-500', '$500-1000', '$1000+'],
                    datasets: [{
                        label: 'Normal Transactions',
                        data: [85000, 95000, 65000, 35000, 3500, 815],
                        backgroundColor: 'rgba(76, 175, 80, 0.7)'
                    }, {
                        label: 'Fraudulent Transactions',
                        data: [120, 145, 85, 110, 22, 10],
                        backgroundColor: 'rgba(244, 67, 54, 0.7)'
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    scales: {
                        y: {
                            type: 'logarithmic',
                            beginAtZero: true,
                            title: {
                                display: true,
                                text: 'Number of Transactions (log scale)'
                            }
                        },
                        x: {
                            title: {
                                display: true,
                                text: 'Transaction Amount'
                            }
                        }
                    }
                }
            });
            
            // Update feature chart
            updateFeatureChart();
        }
        
        // Update feature distribution chart
        function updateFeatureChart() {
            const featureSelect = document.getElementById('feature-select');
            const selectedFeature = featureSelect.value;
            const featureCtx = document.getElementById('feature-chart').getContext('2d');
            
            // Destroy existing chart if it exists
            if (window.featureChart) {
                window.featureChart.destroy();
            }
            
            // Create new chart
            window.featureChart = new Chart(featureCtx, {
                type: 'line',
                data: {
                    labels: Array.from({length: 30}, (_, i) => (i - 15).toFixed(1)),
                    datasets: [{
                        label: 'Normal Distribution',
                        data: Array.from({length: 30}, (_, i) => Math.exp(-0.03 * Math.pow(i - 15, 2)) * 100),
                        borderColor: 'rgba(76, 175, 80, 1)',
                        backgroundColor: 'rgba(76, 175, 80, 0.1)',
                        tension: 0.4,
                        fill: true
                    }, {
                        label: 'Fraud Distribution',
                        data: Array.from({length: 30}, (_, i) => Math.exp(-0.1 * Math.pow(i - 10, 2)) * 100),
                        borderColor: 'rgba(244, 67, 54, 1)',
                        backgroundColor: 'rgba(244, 67, 54, 0.1)',
                        tension: 0.4,
                        fill: true
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    scales: {
                        y: {
                            beginAtZero: true,
                            title: {
                                display: true,
                                text: 'Density'
                            }
                        },
                        x: {
                            title: {
                                display: true,
                                text: `${selectedFeature} Value`
                            }
                        }
                    }
                }
            });
        }
        
        // Train the model
        async function trainModel() {
            if (!uploadedData) {
                alert('Please upload and process data first!');
                return;
            }
            
            // Show training progress
            document.getElementById('training-progress').style.display = 'block';
            document.getElementById('training-results').style.display = 'none';
            
            // Get training parameters
            const epochs = parseInt(document.getElementById('epochs').value);
            const batchSize = parseInt(document.getElementById('batch-size').value);
            const learningRate = parseFloat(document.getElementById('learning-rate').value);
            
            // Update UI
            document.getElementById('total-epochs').textContent = epochs;
            
            // Reset training history
            trainingHistory = {
                accuracy: [],
                val_accuracy: [],
                loss: [],
                val_loss: []
            };
            
            // Create accuracy chart
            const accuracyCtx = document.getElementById('accuracy-chart').getContext('2d');
            window.accuracyChart = new Chart(accuracyCtx, {
                type: 'line',
                data: {
                    labels: [],
                    datasets: [{
                        label: 'Training Accuracy',
                        data: [],
                        borderColor: 'rgba(78, 115, 223, 1)',
                        backgroundColor: 'rgba(78, 115, 223, 0.1)',
                        fill: true
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    scales: {
                        y: {
                            min: 0.9,
                            max: 1.0,
                            title: {
                                display: true,
                                text: 'Accuracy'
                            }
                        },
                        x: {
                            title: {
                                display: true,
                                text: 'Epoch'
                            }
                        }
                    }
                }
            });
            
            // Create validation accuracy chart
            const valAccuracyCtx = document.getElementById('val-accuracy-chart').getContext('2d');
            window.valAccuracyChart = new Chart(valAccuracyCtx, {
                type: 'line',
                data: {
                    labels: [],
                    datasets: [{
                        label: 'Validation Accuracy',
                        data: [],
                        borderColor: 'rgba(54, 185, 204, 1)',
                        backgroundColor: 'rgba(54, 185, 204, 0.1)',
                        fill: true
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    scales: {
                        y: {
                            min: 0.9,
                            max: 1.0,
                            title: {
                                display: true,
                                text: 'Accuracy'
                            }
                        },
                        x: {
                            title: {
                                display: true,
                                text: 'Epoch'
                            }
                        }
                    }
                }
            });
            
            // Simulate training process
            simulateTraining(epochs);
        }
        
        // Simulate training process
        function simulateTraining(epochs) {
            const progressBar = document.getElementById('training-progress-bar');
            const currentEpochElem = document.getElementById('current-epoch');
            const trainAccuracyElem = document.getElementById('train-accuracy');
            const valAccuracyElem = document.getElementById('valid-accuracy');
            
            let currentEpoch = 0;
            
            const trainingInterval = setInterval(() => {
                if (currentEpoch >= epochs) {
                    clearInterval(trainingInterval);
                    finalizeTraining();
                    return;
                }
                
                currentEpoch++;
                currentEpochElem.textContent = currentEpoch;
                
                // Update progress bar
                const progressPercent = (currentEpoch / epochs) * 100;
                progressBar.style.width = `${progressPercent}%`;
                
                // Generate simulated metrics
                const trainAcc = 0.95 + (0.04 * (currentEpoch / epochs)) + (Math.random() * 0.01);
                const valAcc = 0.94 + (0.04 * (currentEpoch / epochs)) + (Math.random() * 0.01);
                const trainLoss = 0.3 - (0.25 * (currentEpoch / epochs)) - (Math.random() * 0.05);
                const valLoss = 0.32 - (0.26 * (currentEpoch / epochs)) - (Math.random() * 0.05);
                
                // Update UI
                trainAccuracyElem.textContent = trainAcc.toFixed(5);
                valAccuracyElem.textContent = valAcc.toFixed(5);
                
                // Save history
                trainingHistory.accuracy.push(trainAcc);
                trainingHistory.val_accuracy.push(valAcc);
                trainingHistory.loss.push(trainLoss);
                trainingHistory.val_loss.push(valLoss);
                
                // Update charts
                window.accuracyChart.data.labels.push(currentEpoch);
                window.accuracyChart.data.datasets[0].data.push(trainAcc);
                window.accuracyChart.update();
                
                window.valAccuracyChart.data.labels.push(currentEpoch);
                window.valAccuracyChart.data.datasets[0].data.push(valAcc);
                window.valAccuracyChart.update();
            }, 1000);
        }
        
        // Finalize training and show results
        function finalizeTraining() {
            // Show results section
            document.getElementById('training-results').style.display = 'block';
            
            // Create transaction monitoring chart
            const transactionsCtx = document.getElementById('transactions-chart').getContext('2d');
            new Chart(transactionsCtx, {
                type: 'line',
                data: {
                    labels: ['00:00', '04:00', '08:00', '12:00', '16:00', '20:00', '24:00'],
                    datasets: [{
                        label: 'Normal Transactions',
                        data: [1200, 800, 2100, 3400, 2900, 2500, 1800],
                        borderColor: 'rgba(76, 175, 80, 1)',
                        backgroundColor: 'rgba(76, 175, 80, 0.1)',
                        fill: true
                    }, {
                        label: 'Flagged Transactions',
                        data: [18, 12, 24, 32, 28, 22, 16],
                        borderColor: 'rgba(244, 67, 54, 1)',
                        backgroundColor: 'rgba(244, 67, 54, 0.1)',
                        fill: true
                    }, {
                        label: 'Confirmed Fraud',
                        data: [2, 1, 3, 5, 4, 3, 2],
                        borderColor: 'rgba(255, 152, 0, 1)',
                        backgroundColor: 'rgba(255, 152, 0, 0.1)',
                        fill: true
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    scales: {
                        y: {
                            title: {
                                display: true,
                                text: 'Transactions per hour'
                            }
                        },
                        x: {
                            title: {
                                display: true,
                                text: 'Time of Day'
                            }
                        }
                    }
                }
            });
            
            // Notify user
            alert('Model training completed successfully! You can now use the fraud detection features.');
        }
        
        // Run t-SNE visualization
        function runTSNE(type) {
            let canvasId, title;
            
            if (type === 'original') {
                canvasId = 'tsne-original-chart';
                title = 'Original Feature Space Visualization';
            } else {
                canvasId = 'tsne-engineered-chart';
                title = 'Engineered Feature Space Visualization';
            }
            
            const ctx = document.getElementById(canvasId).getContext('2d');
            
            // Clear previous chart if exists
            if (window[`tsneChart_${type}`]) {
                window[`tsneChart_${type}`].destroy();
            }
            
            // Generate random data points
            const normalPoints = Array.from({length: 500}, () => ({
                x: Math.random() * 100 - 50,
                y: Math.random() * 100 - 50,
                class: 0
            }));
            
            // Fraud points form clusters
            const fraudPoints = [];
            
            // For original space - fraud points are more scattered
            if (type === 'original') {
                for (let i = 0; i < 3; i++) {
                    const clusterX = Math.random() * 60 - 30;
                    const clusterY = Math.random() * 60 - 30;
                    
                    for (let j = 0; j < 15; j++) {
                        fraudPoints.push({
                            x: clusterX + (Math.random() - 0.5) * 20,
                            y: clusterY + (Math.random() - 0.5) * 20,
                            class: 1
                        });
                    }
                }
            } 
            // For engineered space - fraud points form tighter clusters
            else {
                for (let i = 0; i < 3; i++) {
                    const clusterX = Math.random() * 40 - 20;
                    const clusterY = Math.random() * 40 - 20;
                    
                    for (let j = 0; j < 10; j++) {
                        fraudPoints.push({
                            x: clusterX + (Math.random() - 0.5) * 8,
                            y: clusterY + (Math.random() - 0.5) * 8,
                            class: 1
                        });
                    }
                }
            }
            
            // Create chart
            window[`tsneChart_${type}`] = new Chart(ctx, {
                type: 'scatter',
                data: {
                    datasets: [{
                        label: 'Normal Transactions',
                        data: normalPoints,
                        backgroundColor: 'rgba(76, 175, 80, 0.6)',
                        pointRadius: 4
                    }, {
                        label: 'Fraudulent Transactions',
                        data: fraudPoints,
                        backgroundColor: 'rgba(244, 67, 54, 0.8)',
                        pointRadius: 5
                    }]
                },
                options: {
                    responsive: true,
                    maintainAspectRatio: false,
                    title: {
                        display: true,
                        text: title
                    },
                    scales: {
                        x: {
                            title: {
                                display: true,
                                text: 't-SNE Dimension 1'
                            }
                        },
                        y: {
                            title: {
                                display: true,
                                text: 't-SNE Dimension 2'
                            }
                        }
                    },
                    plugins: {
                        tooltip: {
                            callbacks: {
                                label: function(context) {
                                    return `${context.dataset.label}: (${context.parsed.x.toFixed(2)}, ${context.parsed.y.toFixed(2)})`;
                                }
                            }
                        }
                    }
                }
            });
        }
        
        // Analyze transaction
        function analyzeTransaction() {
            const amount = parseFloat(document.getElementById('transaction-amount').value);
            const isHighValue = amount > 1000 ? 1 : 0;
            
            // Random result for demo (80% chance of normal, 20% chance of fraud)
            const isFraud = Math.random() > 0.8;
            
            // Show appropriate result
            document.getElementById('result-placeholder').style.display = 'none';
            
            if (isFraud) {
                document.getElementById('normal-result').style.display = 'none';
                document.getElementById('fraud-result').style.display = 'block';
                document.getElementById('fraud-probability').textContent = 
                    (85 + Math.random() * 15).toFixed(2) + '%';
            } else {
                document.getElementById('fraud-result').style.display = 'none';
                document.getElementById('normal-result').style.display = 'block';
                document.getElementById('normal-probability').textContent = 
                    (1 + Math.random() * 5).toFixed(2) + '%';
            }
        }
        
        // Initialize the application
        window.onload = function() {
            // Initialize file upload
            initFileUpload();
            
            // Set up feature chart
            updateFeatureChart();
        };
    </script>
</body>
</html>